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Wijnen group

The group focusses on the evaluation of clinical surgical care in pediatric oncology surgery and the improvement of surgical decisions and precision by developing innovative new strategies, like 3d imaging and fluorescence guided surgery and personalized prediction models.

PI: Prof. dr. Marc Wijnen

We are developing a new mouse model for neuroblastoma surgery where we aim to implement our strategy for fluorescence guided surgery.

At the same time we are planning fluorescence guided surgery in patients with tumors that are better suited for this method. E.g. tumors that have shown to be amenable to FGS in adult patients.

"Sometimes less is more in surgery in pediatric oncology."Prof.dr. Marc Wijnen - PI

By developing new 3d techniques we aim to improve our ability to perform (organ sparing) surgery more accurately. This will help prevent long and short time morbidity, while at the same time maintain local control.

Rhabdomyosarcoma

Rhabdomyosarcoma of the head and neck (HN RMS) is a rare type of childhood cancer, which occurs at young age but most children survive long- term. Local treatment is essential to achieve disease control but it causes debilitating long-term adverse events in the majority of survivors, including craniofacial growth deformities and functional impairment of the eyes, pituitary dysfunction, speech difficulties, and dental problems. These treatment-induced adverse events may have lifelong negative impact on quality of life. Various local treatment approaches for HN RMS (i.e., surgery, type of radiation therapy, or a combination thereof) are applied that, while comparable with respect to survival outcome, vary considerably in regard to adverse events. Prediction of adverse events is currently not possible for individual patients. The aim of this project is to develop a decision support model that enables well-informed shared multimodal treatment decision making based on adverse event prediction for individual patients. The research is truncated into three divisions: growth impairment of the craniofacial region, visual, speech and endocrine impairment and the development of a dynamic decision model based on clinical data. The project is carried out in cooperation with Great Ormond Street Hospital (London, UK), Institute Gustave Roussy (Paris, France) and US Health (Jacksonville, FL, USA)

Personalized Sarcoma Care

In Sarcoma local control is of utmost importance for survival. It is often unclear what the best local treatment strategy is, surgery, radiotherapy or a combination of both. However, each strategy has its specific and often strong impact on local adverse effects, a crucial aspect in young patients with a long life expectancy in case they are cured. This project aims to develop a proof-of-concept for a clinical prediction model of Overall Survival and risk of Local recurrence for Sarcoma patients at different time points during treatment. This will come in the form of a prototype app that can be used to evaluate the impact of local-control strategies on survival.

The project will focus on the definition of both a prognostic model and a clear interface to display the predicted outcome resulting from different treatment options.

Expected results are:

1) Individualized local control strategies based on evidence based risk factors 2) To present accurate, personalized treatment and outcome predictions in an easy to understand format for use by both physicians and patients.

The relevance of the project lies in the possibility to dynamically predict the impact of different local-control strategies with respect to survival, and to communicate this to patients and multidisciplinary teams (MDT). For a specific patient, a combination of radiotherapy and surgery might significantly improve survival with respect to surgery alone; in this case one would often chose to combine both therapies and accept an important increase in local adverse effects. In a patient with different patient and tumor characteristics the combination might only marginally improve survival, probably leading to a different decision. The prediction model translated in an app, will optimize local treatment decision making and therefore is completely in line with the KiKa mission by targeting individual maximal cure and minimal burden.

Optimizing rhabdomyosarcoma treatment

Assessing the role of radiological imaging in pediatric rhabdomyosarcoma.

The project consists of four parts:

In over 10% of children with newly diagnosed RMS, chest CT at diagnosis reveals small equivocal pulmonary nodules not meeting criteria for metastases. This group is treated as non-metastatic patients. We will assess whether the presence of equivocal pulmonary nodules affects survival for patients included in the current EpSSG-RMS-2005 study.

Historically, in Europe, early radiological response is measured to evaluate efficacy of chemotherapy. Subsequently, chemotherapy is changed in case of insufficient response. However, the prognostic value of early response is debatable. We will evaluate the prognostic value of early radiological response in patients included in the SIOP-MMT-95 protocol.

Patients with RMS are subject to extensive radiologic surveillance after therapy completion. We will examine whether this radiologic surveillance leads to earlier detection of relapse and subsequently improved survival.

We will determine the psychological impact of radiologic surveillance on patients and parents and explore their needs in routine follow-up programs.

Results of this study will directly impact current treatment strategies and will be translated into the new European Frontline and Relapse-RMS study (EpSSG FaR-RMS study, starting end 2018).

Shared decision making in orthopaedic oncology surgery

Using quality of life prediction models we aim to make the decision of what type of local therapy us moost tailored to the needs of the individual patient more consistent and useful